TY - GEN
T1 - Fusion scheme for automatic and large-scaled built-up mapping
AU - Forget, Yann
AU - Shimoni, Michal
AU - Lopez, Juanfran
AU - Linard, Catherine
AU - Gilbert, Marius
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/10/31
Y1 - 2018/10/31
N2 - As more and more geospatial data are produced, Big Earth data is becoming a new key to the understanding of the Earth. Such opportunity also comes with new issues and challenges related to the massive and heteregenous amount of data to process and to analyse. The present work explores the use of three types of Earth Observation (EO) data in order to automatically classify built and non-built areas in Africa using a machine learning classifier: SAR (Sentinel) and optical (Landsat) imagery, and the OpenStreetMap (OSM) database as training data. Experimental results in ten african cities show that the use of satellite data from multiple sensors improves the performance of the classifiers in these areas. They also show that using crowd-sourced geospatial databases such as OSM as training data leads to similar accuracies than when relying on field surveys or hand-digitalized datasets.
AB - As more and more geospatial data are produced, Big Earth data is becoming a new key to the understanding of the Earth. Such opportunity also comes with new issues and challenges related to the massive and heteregenous amount of data to process and to analyse. The present work explores the use of three types of Earth Observation (EO) data in order to automatically classify built and non-built areas in Africa using a machine learning classifier: SAR (Sentinel) and optical (Landsat) imagery, and the OpenStreetMap (OSM) database as training data. Experimental results in ten african cities show that the use of satellite data from multiple sensors improves the performance of the classifiers in these areas. They also show that using crowd-sourced geospatial databases such as OSM as training data leads to similar accuracies than when relying on field surveys or hand-digitalized datasets.
UR - http://www.scopus.com/inward/record.url?scp=85063152929&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518266
DO - 10.1109/IGARSS.2018.8518266
M3 - Conference contribution
AN - SCOPUS:85063152929
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2072
EP - 2075
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -